Supplementary Figures displaying posterior probability distributions of all simulation scenarios discussed in The Influence of Stellar Contamination on the Interpretation of Near-Infrared Transmission Spectra of Sub-Neptune Worlds around M-dwarfs. Arxiv link to the paper: https://arxiv.org/abs/1912.04389 The directory structure is as follows: 1) 30ppm_JWST_NIRISS_BIAS: Cases with varying levels of stellar contamination and varying planetary atmospheric scenarios (clear/cloudy/high-metallicity) over the JWST NIRISS-like bandpass with 30ppm spectro-photometric precision. Here we show that there is a bias incurred in the retrieval results when fitting with a model not accounting for the stellar contamination correction. 2) 30ppm_JWST_NIRCAM_BIAS: Case of 14% spot and 63% faculae over the JWST-NIRCam like bandpass with 30ppm precision for a cloudy atmosphere planet. 3) JWST_NIRISS_VARY_PRECISION: Cases with varying levels of stellar contamination for a high-metallicity+cloudy planetary atmosphere exploring different JWST precisions from 15-120ppm. Here we show that the bias incurred when retrieving with the uncorrected atmosphere model persists over our grid of JWST sensitivities with increasing levels of stellar contamination. However, this bias is not noteworthy for spot-covering fractions below 1%. 4) 30ppm_JWST_NIRISS_NO_IMPROVEMENT_WITH_JOINT_RETRIEVAL_OF_STELLAR_SPECTRUM: Case of 12% spot covering fraction with a high-metallicity+cloudy planetary atmosphere (with 30ppm precision over NIRISS-like bandpass) shows no improvement in the precision of planetary temperature, metallicity, and carbon-to-oxygen ratio despite acquiring improved constraints on the stellar contamination parameters directly from the disk-integrated spectrum of the star. 5) 30ppm_JWST_NIRISS_STELLAR_MODEL_DIFF_BIAS: Case of 12% spot covering fraction with a high-metallicity+cloudy planetary atmosphere (with 30ppm precision on the NIRISS-like bandpass range) incorporating "realistic" stellar contamination with a different stellar model. Here we show that there is a bias that arises in the retrievals due to generational stellar model differences (also mimicking stellar model-data differences in our example case) despite including the correction for stellar contamination. Our work suggests that planetary parameter retrievals, despite correcting for stellar contamination are heavily dependent on an accurate characterization of the stellar photosphere. *** In addition to the supplementary figures in the above mentioned directories, you can also access a high-resolution version of Figure 10 from the paper, accompanied by the full retrieval corner plot illustrating bias induced due to stellar model differences. *These figures have made use of the pygtc plotting routine for displaying posterior probability distribution corner plots (Bocquet et al, (2016), pygtc: beautiful parameter covariance plots (aka. Giant Triangle Confusograms), Journal of Open Source Software, 1(6), 46, doi:10.21105/joss.00046)